Automated informatics may increase the detection rate of suspicious cases of human trafficking—a preliminary study

Abstract Objectives Worldwide, there is an estimated 40.3 million victims trapped in modern day slavery, including 24.9 million in forced labor and 15.4 million in forced marriage. A majority of labor and sex trafficking survivors report at least one healthcare encounter during their victimization. An approach to an informatics technology solution for identifying trafficked persons in real time, in the hospital / emergency department settings is the primary focus of this paper. Materials and methods Octavia, a software application implemented in 3 California hospitals, scanned all patient encounters for social and clinical determinants that are consistent predictors of HT. Any encounter that matched these criteria was forwarded to a specially trained High-Risk Navigator who screened the data and when able, made direct contact in an effort to build rapport and possibly provide victim assistance. Results During the observation period, the automated scanning of hospital patient encounters resulted in a notable increase in the detection of persons who had a likelihood of being trafficked when compared to a pre-project baseline. Discussion Our experience demonstrated that automated technology is useful to assist healthcare providers in identification of potentially trafficked persons, improving the likelihood of care provision.


Background and significance
Around the world, there is an estimated 50 million victims trapped in "modern day slavery," including 28 million in forced labor and 22 million in forced marriage.Almost one in eight of all those in forced labour are children.More than half of these are in commercial sexual exploitation. 1The exact number of victims is largely unknown, as researchers face difficulty in accurately and adequately assessing prevalence and incidence.The interaction with the medical care system may be one of the best opportunities for trafficked persons to be identified and offered assistance.][4][5][6][7][8][9][10][11][12] In recognition, health care organizations are advocating for and adopting procedures to better identify vulnerable patients and connect them to care.

Objectives
Analytical tools are commonly used to process categorical risk assessments, such as risk for cardiac event or posthospital discharge complication.4][15][16][17][18] We found no published trafficking research related to the application of computational models in the hospital/emergency department settings specifically.An approach to an informatics technology solution for identifying trafficked persons in real time, in the hospital/emergency department settings is the primary focus of this article.

Materials and methods
From 2018 to 2022, an application known as "Octavia," a Whole Person Care software platform, was employed in 3 hospitals of CommonSpirit Health, the largest nonprofit healthcare system in the country.Electronic health record (EHR) data is sent 4 times per hour to Octavia via secure unidirectional API.Octavia organizes these data into a clinically relevant, HIPAA secure shared database for use in direct patient navigational services, outcomes inquiry, and quality improvement analyses.Octavia's sentry function applies a query rule set to all new incoming data and provides alerts when a match is detected.As the technology piece of the anti-HT team, the Octavia sentry was used to alert a High-Risk Patient Navigator (HRPN) when a match with certain query rule sets or "computational phenotypes" was detected.HRPN are specially trained healthcare professionals, some with lived experience, able to screen for potential abuse, neglect, or violence (ANV), including HT.
The concept of computational phenotype has previously been used in neural and behavioral science, and precision medicine. 19,20In this project, the computational phenotypes consisted of combinations of keywords and other data points that were consistent predictors of persons at-risk of being trafficked (see Table 1).Attributes differed between male and female persons, and included clinical and nonclinical (social determinants of health) attributes, some specific to the geographical region.Intentional effort was made to incorporate additional keywords and diagnoses to ensure inclusion of male patients and those persons who are labor-trafficked, as an omission of labor trafficking in anti-trafficking efforts has been documented in the literature. 21Social work notes, care coordinator notes, ICD-10 codes, and emergency department utilization records were mined for keyword matches.Keyword phrases were run retrospectively against results data, and internally validated against identified cases.As results and experiential patterns were observed by the project team, the key word phrases, attributes, and inference logic were continuously adjusted to improve the sensitivity and specificity of the computational phenotype results.
Upon receiving an alert, the HRPN screened the clinical and social data available and when able, made direct contact with the person.To maximize development of rapport and trust, effort was made to provide longitudinal support-consistent contact with the same HRPN upon every subsequent clinical visit following the visit which prompted the initial alert.

Results
The observational period for the CommonSpirit Health Central Coast project included 4th quarter 2019 through 3rd quarter 2021.The Octavia sentry alerted to an average of 1 to 8 potential cases daily (out of an average of 440 total daily encounters) for HRPN review.About 43.17% of the alerts were reviewed by the HRPN.Of those reviewed, 24% of cases were considered "highly suspicious" or known confirmed cases of HT, totaling 184 during the 23-month observational period.Comparatively, as part of CommonSpirit Health's commitment to care and protection of trafficked persons, and in support of a grant proposal to the United States Department of Justice, preproject baseline data was released for 2017-2018 that included 10 total patients per year identified as "possible" cases of ANV including HT in the 3 Central Coast hospitals.
Resourcing of a HRPN during this observational period was affected by the COVID pandemic response with high hospital census combined with limited staffing, requiring the HRPN to divert time from the anti-HT project to direct clinical patient care.This is reflected in the 47.3% alert response, with no alert response in September 2020 (see Table 2).
Figure 1 illustrates the number of cases reviewed by the HRPN per month in response to Octavia sentry alerts.Figure 2 depicts the number of the cases reviewed that were determined by the HRPN to be "highly suspicious."

Discussion
While the preliminary work during this observational period shows a promising process in identifying trafficked persons, it is acknowledged that limitations exist.Limitations of the  presented work and its subsequent conclusions include the small sample size accrued from 3 mid-sized California hospitals.The population of HT victims in California who accessed health care in these systems in the study time frame, while large, cannot be presumed representative of all patients experiencing HT.The computational phenotype keyword dataset is iteratively being improved and some biases have been identified.One limitation in early iterations was the words "lesbian," "gay," "bisexual," and "homosexual" were not included in the computational phenotype keyword dataset.Additionally, it must be recognized that the identification, assessment, and policy making regarding sex trafficking are disproportionately greater than that directed toward labor trafficking. 21As such, it is possible that our methods of identification may have also been biased toward criteria consistent with sex trafficking.
The determination of a case to be "highly suspicious" was ultimately a subjective decision of the HRPN, based on the information available at the time.This subjectivity persists until a case is confirmed by either self-report or law enforcement report.Despite active follow-up by care teams, the vast majority of atrisk cases remain classified as "highly suspicious" without the potential victim clearly confirming and/or agreeing to formally report their case.It is not possible to know the true denominator of HT cases passing through our facility doors thus positive predictive value calculation is ultimately limited.
The practice of patient profiling in healthcare, including concerns regarding the possible stigmatization of vulnerable populations, has been raised.With inclusion of a wide breadth of elements, not confined to the attributes most often associated with profiling (including race, gender, disability, social class), the unintended consequences of discrimination or stigmatization of any single population hopes to be mitigated.The Center for Disease Control's has added "suspected human trafficking" diagnosis codes (eg, ICD-10 code T76.5-Forced sexual exploitation, suspected) to the International Classification of Diseases (ICD). 37though Octavia does look for these codes in the diagnoses, it is worth considering that adding these diagnoses or verbiage to the medical chart without timely patient knowledge might put the patient at risk if an abuser finds this information on the discharge paperwork and medical records.This concern is further magnified by the recent implementation of the information blocking provisions of the 21st Century CURES Act, which penalizes the delayed transmission of medical documentation to patients outside of narrowly defined exceptions. 38As immediate electronic access to patient medical documentation becomes mainstream, patients will likely face increased risk in seeking medical attention due to the de facto access maintained by abusers and traffickers via coercion and violence.Individual hospital administrations are responsible for developing and implementing policies compliant with these regulations, balancing medical transparency and patient safety, which may be greatly benefited by automated alerting and tracking of a high-risk patient population.
While safeguards were in place to prevent bias, researcher affiliation with the software company that produces the Octavia application (researchers D.O.D. and K.N.) could have led researchers to inadvertently misattribute outcomes as due to the software versus other factors (measured or unmeasured).While independent researchers unaffiliated with the software company were involved in all phases of the project and gave approval for final manuscript submission, their collaboration with team members involved in the software company could have nevertheless introduced bias.

Conclusion
Per the U.S. Department of State, data and statistics may not reflect the full scope of the forced labor problem, due to the hidden nature of the crime, challenges in identifying individual victims, gaps in data accuracy and completeness, and significant barriers regarding the sharing of victim information among various stakeholders. 39Thus, efforts to increase the detection of highly suspicious cases of HT, and the offer of services in a safe, trauma-informed environment, particularly from someone with whom the person has developed a rapport, may be a worthy strategy to pursue.It is our hope that this preliminary study will encourage further exploration in the use of automated technology to improve the detection of possible trafficked persons, as well as consideration of the merits of providing longitudinal, rapport-developing support in a healthcare setting.Additionally, we advocate for traumainformed health system policies that connect people experiencing trafficking to the provision of longitudinal services.These may include connecting trafficked patients to housing as well as access to specialty medical clinics dedicated to the follow-up and care with a requirement that all providers working in the clinic receive the appropriate training, support, and resources.Trauma-informed specialty clinics have already been implemented in the CommonSpirit Health System (Fresno, CA) and are demonstrating promising results.The CA Central Coast hospitals that sponsored this project launched a specialty clinic in the autumn of 2022.

184 24. 18 Figure 1 .
Figure 1.High Risk Patient Navigator number of cases reviewed.Bar colors provide visual differentiation in months.This graph depicts fluctuation in available capacity of navigators to review encounters over the course of the program to date.Dips in Navigator's capacity are attributed to other priorities (eg, COVID-19 support) and demands (paid leave and etc.) during the period suggesting a single Navigator was not able to review all alerts, all of the time.

Table 2 .
Results of Octavia alert volume, case review, and identification.